1H nuclear magnetic resonance (NMR) is widely used to investigate the metabolic state of biological samples. Its non-invasive nature and ability to detect multiple compounds allow it to follow complex biochemical processes over time at a high level of detail leading to its use in "metabolomics" or characterization of the metabolite response of living systems to pathophysiological stimuli or genetic modification. It generates spectra with a large number of resonances from hundreds of related samples. The use of pattern recognition techniques here have been restricted by two factors: 1) small systematic variations in frequency caused by small differences in temperature, pH, etc. and 2) use of principal component analysis (PCA) for the identification of the mathematical components of the variation in the dataset, rather then true physical sources for dynamic changes. The variations in frequency have led to "binning" the spectra to only a few hundred points while the use of PCA to identify underlying patterns in the datasets makes finding physically meaningful metabolic patterns hard. In this R33 we address these problems by robust pretreatment of high resolution NMR data and then applying Bayesian Spectral Decomposition (BSD) and constrained Non-negative Matrix Factorization (cNMF) to uncover the underlying metabolic patterns that describe the change in the system. There are five main Specific Aims Specific Aim I: Develop a semi-automated preprocessing procedure for series of high resolution 1H NMR spectra of urine to improve spectral quality to enable PR procedures to identify any underlying biochemically relevant spectral patterns. Specific Aim II: Implement Bayesian Spectral Decomposition (BSD) as a practical easy-to-use spectral analysis procedure. Specific Aim III. Implement constrained Non-negative Matrix Factorization (cNMF) as a practical easy-to-use spectral analysis procedure Specific Aim IV. Implement BSD as parallel code on a Linux cluster. Specific Aim V. Apply BSD and cNMF to analyze a series of NMR spectra of urine acquired from toxicology studies of rats and mice that have been preprocessed by the techniques of Specific Aim I.